Measurement device and processor configured to execute measurement method

ABSTRACT

A measurement device adapted to cooperate with a three-dimensional image is provided. The three-dimensional image includes a plurality of three-dimensional positioning points. The measurement device comprises: a first camera unit for providing a two-dimensional image; an analysis module for analyzing the two-dimensional image to define a plurality of two-dimensional positioning points; a matching module for making the two-dimensional positioning points correspond to the three-dimensional positioning points, respectively, to generate a three-dimensional model; an input module for receiving a starting point and a destination in the two-dimensional image; a measurement module for obtaining first position information and second position information that correspond to the starting point and the destination respectively and calculating data; and an output module. A processor configured to execute a measurement method is also provided.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims the priority benefit of US provisionalapplication Ser. No. 62/341,640, filed on May 26, 2016, and Taiwanapplication serial No. 106109804, filed on Mar. 23, 2017. The entiretyof the above-mentioned patent applications are hereby incorporated byreferences herein and made a part of specification.

BACKGROUND OF THE INVENTION Field of the Invention

The disclosure relates to a measurement device and a processorconfigured to execute a measurement method.

Description of the Related Art

With the development of e-commerce, almost all products can be purchasedonline. For example, glasses, clothes or wearable electronic devices arenow can be purchased online anytime. However, this kind of productscannot be tried on to find out a suitable size, and the sizes of theproducts are usually inappropriate. The subsequent exchange or returnprocess of these products are increased, which blocks the development ofe-commerce.

BRIEF SUMMARY OF THE INVENTION

According to an aspect of the disclosure, a measurement device adaptedto cooperate with a three-dimensional image is provided. Thethree-dimensional image includes a plurality of three-dimensionalpositioning points.

The measurement device comprises a first camera unit, an analysismodule, a matching module, an input module, a measurement module and anoutput module. The first camera unit provides a two-dimensional image.The analysis module analyzes the two-dimensional image to define aplurality of two-dimensional positioning points in the two-dimensionalimage. The matching module makes the two-dimensional positioning pointscorrespond to the three-dimensional positioning points, respectively, togenerate a three-dimensional model. The input module receives a startingpoint and a destination in the two-dimensional image.

The measurement module obtains first position information and secondposition information that correspond to the starting point and thedestination respectively from the three-dimensional positioning points,according to the three-dimensional model, and calculates data based onthe first position information and the second position information. Theoutput module outputs the data.

According to another aspect of the disclosure, a processor configured toexecute a measurement method provided. The processor configured toexecute a measurement method is adapted to cooperate with athree-dimensional image. The three-dimensional image includes aplurality of three-dimensional positioning points.

The processor executes the step as follows: controlling a camera unit tocapture a target object to obtain a two-dimensional image via a firstcamera unit, analyzing the two-dimensional image to define a pluralityof two-dimensional positioning points in the two-dimensional image,making the two-dimensional positioning points correspond to a pluralityof three-dimensional positioning points, respectively, to generate athree-dimensional model; receiving a starting point and a destination inthe two-dimensional image, obtaining first position information andsecond position information that correspond to the starting point andthe destination, respectively, from the three-dimensional positioningpoints according to the three-dimensional model and calculating databased on the first position information and the second positioninformation, and outputting the data.

In embodiments, the measurement device and the processor configured toexecute the measurement method are provided. The two-dimensionalpositioning points correspond to the three-dimensional positioningpoints to generate the three-dimensional model. The data for thespecific portion of the target object between any two three-dimensionalpositioning points is measured according to the three-dimensional model.

Compared to a two-dimensional measurement, the data for the specificportion of the human body that is measured between the twothree-dimensional positioning points is more precise. The measurementdevice can be applied in online purchases. The data is transmitted tothe merchant. Therefore, the buyer does not need to be at a shop/storeand the merchant can also provide the customized product for the buyeraccording to the data for the specific portion of the human body.

BRIEF DESCRIPTION OF THE DRAWINGS

These and other features, aspects and advantages of the disclosure willbecome better understood with regard to the following embodiments andaccompanying drawings.

FIG. 1 is a measurement device in an embodiment;

FIG. 2 is a flow chart of a measurement method that is executed by aprocessor in an embodiment;

FIG. 3 is a two-dimensional image in an embodiment; and

FIG. 4 is a three-dimensional model in an embodiment.

DETAILED DESCRIPTION OF THE EMBODIMENTS

Please refer to FIG. 1. FIG.1 is a measurement device 100 in anembodiment. In an embodiment, a measurement device 100 includes a firstcamera unit 130, an analysis module 140, a matching module 150, an inputmodule 160, a measurement module 170 and an output module 180. In anembodiment, the measurement device 100 further includes a second cameraunit 120 and a storage unit 110.

In an embodiment, the first camera unit 130 is a camera unit (such asthe camera unit of a smartphone) for capturing two-dimensional images.

In an embodiment, the second camera unit 120 is a depth camera unit(such as a depth camera).

In an embodiment, the differences among the second camera unit 120, thefirst camera unit 130 and a conventional camera are that the firstcamera unit 130 is configured to store a three-dimensional image of areal world as a two-dimensional (X-Y axis) image, and the second cameraunit 120 is configured to measure a Z-axis distance between each imagepoint. Consequently, the stored information is the three-dimensionalspatial information.

In an embodiment, the second camera unit 120 includes at least two lensfor capturing images, respectively. The second camera unit 120 comparesthe differences between the images from the two lens by using a currentimage processing algorithm to obtain the Z-axis information for theimage point depth.

In an embodiment, by transmitting infrared rays to an imaged target, adistance between each reflection point and the second camera unit 120 iscalculated according to reflected information.

In an embodiment, the analysis module 140, the matching module 150, themeasurement module 170 are individually or integratedly implemented by amicrocontroller, a microprocessor, a digital signal processor, anapplication specific integrated circuit (ASIC) or a logic circuit.

In an embodiment, the input module 160 and/or the output module 180 isimplemented by a user interface. In an embodiment, a user inputs amessage via the user interface. The measurement device 100 calculatesaccording to the message to get a result. Then, the result is displayedby a user interface.

In an embodiment, the storage unit 110 is configured to store variouskinds of information in a database. In an embodiment, the storage unit110 is a memory, a hard disk, or a mobile memory card, which is notlimited herein.

Please refer to FIG. 2 to FIG. 4. FIG. 2 is a flow chart of ameasurement method 200 that is executed by a processor in an embodiment.FIG. 3 is a two-dimensional image 300 in an embodiment. FIG. 4 is athree-dimensional model 400 in an embodiment. The embodiments describedhereinafter can be implemented by the measurement device 100 in FIG. 1.A sequence for steps of the measurement method 200 executed by theprocessor is not limited by the following embodiments. The sequence ofsteps is adjusted according to requirements.

In an embodiment, the measurement device 100 captures a target objectvia the second camera unit 120 to obtain a three-dimensional image and aplurality of three-dimensional positioning points a′ to f′ of thethree-dimensional image. The three-dimensional image and thethree-dimensional positioning points a′ to f′ are stored in the storageunit 110.

In an embodiment, the measurement device 100 does not include the secondcamera unit 120. The three-dimensional image of the target object andthe three-dimensional positioning points a′ to f′ of thethree-dimensional image needs to be obtained (from external devices).For example, in an embodiment, the measurement device 100 is asmartphone, which includes the first camera unit 130, but not includesthe second camera unit 120.

In this case, the measurement device 100 establishes a wire or wirelesscommunication with an external second camera unit 120 (such as a depthcamera placed at a fixed position) to obtain the three-dimensional imageof the target object and the three-dimensional positioning points a′ tof′ of the three-dimensional image from the second camera unit 120. Thethree-dimensional image of the target object and the three-dimensionalpositioning points a′ to f′ of the three-dimensional image are stored tothe storage unit 110 by the measurement device 100.

In an embodiment, the measurement device 100 includes the first cameraunit 130 and the second camera unit 120. Thus, the measurement device100 obtains the three-dimensional image of the target object and thethree-dimensional positioning points a′ to f′ of the three-dimensionalimage directly from the second camera unit 120.

In other words, in an embodiment, the three-dimensional image of thetarget object and the three-dimensional positioning points a′ to f′ ofthe three-dimensional image are obtained by the measurement device 100before step 230.

In an embodiment, the target object is a face, a finger, an arm, limbsor the whole human body. In embodiments, the target object is a portionof the human body, which is not limited herein. In the followingembodiment, the target object is a face.

In an embodiment, the second camera unit 120 is configured to capture aface image. While the face image is captured, the second camera unit 120scans the face to obtain depth information of each point of the faceand/or brightness information of red light, green light and blue light.Thus, the three-dimensional image of the face and the three-dimensionalpositioning points a′ to f′ of the three-dimensional image are obtained.

In an embodiment, the second camera unit 120 obtains thethree-dimensional positioning points of the face by using a current facerecognition algorithm (such as, by using feature points, skin colorinformation, profile information) For example, 68 three-dimensionalpositioning points (all the points as shown in FIG. 4 are thethree-dimensional positioning points) of the face are used.

In step 210, a two-dimensional image 300 is provided by the first cameraunit 130.

In an embodiment, as shown in FIG. 3, the first camera unit 130 (such asthe camera unit of the smartphone) captures a front face image, a sideface image or a side-front face image to obtain the two-dimensionalimage 300.

In step 220, the analysis module 140 analyzes the two-dimensional image300 to define the two-dimensional positioning points a to f (as shown inFIG. 3) in the two-dimensional image 300.

In an embodiment, the human face features is pre-stored into a facefeature database by the measurement device 100. Thus, the analysismodule 140 obtains the human face features from the face featuredatabase and compares the human face features with the two-dimensionalimage 300 to define the two-dimensional positioning points a to f. Theanalysis module 140 obtains a plurality of two-dimensional plottedcoordinates that correspond to the two-dimensional positioning points ato f, respectively, in the two-dimensional image 300. In an embodiment,for example, the coordinate (X, Y) for the two-dimensional positioningpoint a in the two-dimensional image 300 is (100, 110). The coordinate(X; Y) for the two-dimensional positioning point b in thetwo-dimensional image 300 is (120, 110).

In an embodiment, the two-dimensional positioning points a to frepresent feature positioning points in the face, respectively, such as,an inner corner of the eye, an outer corner of the eye, a left corner ofthe mouth, a right corner of the mouth and so on.

In an embodiment, a current face recognition algorithm is applied todetermine the two-dimensional positioning points a to f, which is notlimited herein.

In FIG. 3, only the positioning points a to f are exemplified as thetwo-dimensional positioning points. In an embodiment, the number of thetwo-dimensional positioning points that the analysis module 140 obtainsis more (for example, 68 two-dimensional positioning points areobtained), which is not limited herein.

In step 230, the matching module 150 makes the two-dimensionalpositioning points a to f correspond to the three-dimensionalpositioning points a′ to f′, respectively, to generate athree-dimensional model 400.

In an embodiment, the three-dimensional model 400 is used to present thethree-dimensional positioning points a′ to f′ that correspond to thetwo-dimensional positioning points a to f, respectively.

In an embodiment, as shown in FIG.4, the matching module 150 makes thetwo-dimensional positioning points a to f correspond to thethree-dimensional positioning points a′ to f′, respectively, to generatethe three-dimensional model 400. The three-dimensional positioningpoints a′ to f′ are shown on the three-dimensional model 400.

In the embodiment, each group of the two-dimensional positioning point(such as the two-dimensional positioning point e) and the correspondingthree-dimensional positioning point (such as the three-dimensionalpositioning point e′) indicate the same position (such as the leftcorner of the mouth) of the human face.

In other words, in step 230, the two-dimensional positioning points a tof are mapped to the three-dimensional positioning points a′ to f′,respectively, to generate the three-dimensional model 400.

In an embodiment, since the second camera unit 120 (such as the depthcamera) is not easy to get by the user, in the above step, thethree-dimensional image is pre-captured via the second camera unit 120(such as the depth camera). Then, the three-dimensional image and thethree-dimensional positioning points a′ to f′ are stored into thestorage unit 110.

In subsequent steps, the first camera unit 130 (such as the camera unitof the smartphone) is used to capture the face image from differentangles to obtain the two-dimensional images 300. Then, thetwo-dimensional positioning points a to f in the two-dimensional image300 are mapped to the obtained three-dimensional positioning points a′to f′ via the matching module 150 to generate the three-dimensionalmodel 400 (for example, the three-dimensional model 400 is displayed ona display screen of the smartphone). The three-dimensional positioningpoints a′ to f′ are obtained from the storage unit 110 by thesmartphone.

In an embodiment, the storage unit 110 is configured at a cloud server.The matching module 150 is implemented by the processor of thesmartphone. The smartphone downloads the three-dimensional positioningpoints a′ to f′ from the storage unit 110 at the cloud server via thenetwork to calculate.

Details that the two-dimensional positioning points a to fin thetwo-dimensional image 300 are mapped to the three-dimensionalpositioning points a′ to f′ by the matching module 150 to generate thethree-dimensional model 400 is described hereafter.

In an embodiment, the three-dimensional positioning points a′ to f′presented in the three-dimensional model 400 in FIG.4 correspond tothree-dimensional plotted coordinates, respectively. In an embodiment,for example, the coordinate (X, Y, Z) for the three-dimensionalpositioning point a′ in the three-dimensional model 400 is (100, 110,200). The coordinate (X, Y, Z) for the three-dimensional positioningpoint b′ in the three-dimensional model 400 is (120, 110, 205).

In an embodiment, the matching module 150 is configured to rotate,translate the two-dimensional image 300, or adjust the size of thetwo-dimensional image 300 to make the two-dimensional positioning pointsa to f correspond to the three-dimensional positioning points a′ to f′,respectively.

In an embodiment, the matching module 150 makes the two-dimensionalpositioning points a to f correspond to the three-dimensionalpositioning points a′ to f′, respectively, according to a calibrationparameter of the camera.

In an embodiment, the matching module 150 makes the two-dimensionalpositioning points a to f correspond to the three-dimensionalpositioning points a′ to f′, respectively, via a perspective projectionmodel of the camera.

In an embodiment, when the number of the three-dimensional positioningpoints in a universal reference frame is n, the matching module 150makes the two-dimensional positioning points correspond to thethree-dimensional positioning points, respectively, by rotating ortranslating the two-dimensional image according to the two-dimensionalpositioning points, the calibration parameter of the camera and the sixdegree of freedom. The perspective projection model of the camera ispresented as follows:

sp _(c) =K[R[T]p _(w) sp _(c) =K[R[T]p _(w)

wherein p_(w)=[x y z 1]^(T) is a homogeneous world positioning point,the symbols x, y and z represent a real world coordinate system formedfrom the camera position to the homogeneous world positioning point. Thesymbol x represents an X-axis coordinate position. The symbol yrepresents a y-axis coordinate position. The symbol z represents aZ-axis coordinate position. p_(c)=[u v 1]^(T) is a correspondinghomogeneous image point. The symbol represents an X-axis position forthe two-dimensional X-Y image. The symbol v represents a Y-axis positionfor the two-dimensional X-Y image. The symbol K is a matrix of thecalibration parameter of the camera. The symbol s is a scale factor. Thesymbols R and T are the three-dimensional rotation and thethree-dimensional movement required to take by the camera, respectively.Thus, the formula is presented as follows:

${s\begin{bmatrix}u \\v \\1\end{bmatrix}} = {{\begin{bmatrix}f_{x} & \gamma & u_{0} \\0 & f_{y} & v_{0} \\0 & 0 & 1\end{bmatrix}\begin{bmatrix}r_{11} & r_{12} & r_{13} \\r_{21} & r_{22} & r_{23} \\r_{31} & r_{32} & r_{33}\end{bmatrix}}\begin{bmatrix}x \\y \\z \\1\end{bmatrix}}$

wherein the symbols f_(x) and f_(y) are proportion focal lengths. Thesymbol γ is a tilt parameter. In an embodiment, the tilt parameter isset to 0. The symbol (u₀, v₀) is a main positioning point. Thus, withthe formula, the two-dimensional positioning points are made tocorrespond to the three-dimensional positioning points, respectively, bythe rotation or translation of the two-dimensional image according tothe two-dimensional positioning points, the calibration parameter of thecamera and the six degree of freedom.

The symbol R includes an X-axis, Y-axis and Z-axis rotation coefficientsγ₁₁ to y₃₃. In an embodiment, the X-axis rotation angle is α. The Y-axisrotation angle is β. The Z-axis rotation angle is γ. The symbol R isdefined as follows:

$R = {{\begin{bmatrix}r_{11} & r_{12} & r_{13} \\r_{21} & r_{22} & r_{23} \\r_{31} & r_{32} & r_{33}\end{bmatrix}} = \begin{bmatrix}{{\cos (\beta)}{\cos (\gamma)}} & {{{\sin (\alpha)}{\sin (\beta)}{\cos (\gamma)}} - {{\cos (\alpha)}{\sin (\gamma)}}} & {{{\sin (\alpha)}{\sin (\gamma)}} + {{\cos (\alpha)}{{sing}(\beta)}{\cos (\gamma)}}} \\{{\cos (\beta)}{\sin (\gamma)}} & {{{\cos (\alpha)}{\cos (\gamma)}} + {{\sin (\alpha)}{\sin (\beta)}{\sin (\gamma)}}} & {{{\cos (\alpha)}{\sin (\beta)}{\sin (\gamma)}} - {{\sin (\alpha)}{\cos (\gamma)}}} \\{- {\sin (\alpha)}} & {{\sin (\alpha)}\cos \; (\beta)} & {{\cos (\alpha)}{\cos (\beta)}}\end{bmatrix}}$

wherein the symbol t₁, t₂, t₃ in the equation T=[t₁ t₂ t₃]^(T) representan X-axis displacement, a Y-axis displacement and a Z-axis displacement,respectively. According to the equation sp_(c)=K[R[T]p_(w), thetwo-dimensional image coordinates for the six two-dimensionalpositioning points a to f (P_(c)) are introduced to correspond to theknown three-dimensional real-world coordinates for the sixthree-dimensional positioning points a′ to r(P_(w)). A generalized leastsquare method is applied to obtain R and T. Thus, the two-dimensionalpositions for the two-dimensional positioning points a to f in thetwo-dimensional image coordinate system are calibrated. The distancebetween any two points taken from the three-dimensional model representsa real distance (that is, the data).

In an embodiment, the algorithm for matching the two-dimensionalpositioning points to the three-dimensional positioning points isvarious, which is not limited herein.

In step 240, the input module 160 receives a starting point and adestination in the two-dimensional image 300. In an embodiment, thestarting point is a positioning point of the inner corner of the lefteye. The destination is a positioning point of the inner corner of theright eye.

In an embodiment, the user selects any two three-dimensional positioningpoints from the three-dimensional model 400 shown in FIG. 4 as thestarting point and the destination. In an embodiment, for example, theuser selects the three-dimensional positioning point a′ as the startingpoint, and selects the three-dimensional positioning point b′ as thedestination. In another embodiment, the user selects thethree-dimensional positioning point c′ as the starting point, andselects the three-dimensional positioning point d′ as the destination.

In step 250, the measurement module 170 obtains first positioninformation and second position information that correspond to thestarting point and the destination, respectively, from thethree-dimensional positioning points according to the three-dimensionalmodel. The measurement module 170 calculates to obtain the data based onthe first position information and the second position information.

In step 260, the output module 180 outputs the data.

In an embodiment, when the user selects the three-dimensionalpositioning point a as the starting point and selects thethree-dimensional positioning point b as the destination, themeasurement module 170 calculates to obtain the data based on theposition information of the starting point (i.e., the three-dimensionalpositioning point a′) and the destination (i.e., the three-dimensionalpositioning point b′). Thus, the distance (such as 2 centimeters)between the inner corners of the left and right eyes on the face isobtained. The position information refers to the three-dimensionalplotted coordinates.

In an embodiment, when the user selects the three-dimensionalpositioning point c as the starting point and selects thethree-dimensional positioning point d as the destination, themeasurement module 170 calculates to obtain the data based on theposition information of the starting point (i.e., the three-dimensionalpositioning point c′) and the destination (i.e., the three-dimensionalpositioning point d′). Thus, the distance (such as 18 centimeters)between the inner corners of the left and right eyes on the face isobtained.

Therefore, the user only needs to select any two three-dimensionalpositioning points from the three-dimensional model 400 as the startingpoint and the destination to obtain the actual data (such as thedistance) between the two points.

In an embodiment, the steps 220 to 270 are performed via an application(APP). The application is installed on a smart device (such as a tablet,a smartphone and so on). The smart device captures the two-dimensionalimage 300 and analyzes the two-dimensional positioning points a to f inthe two-dimensional image 300. The smart device obtains thethree-dimensional positioning points a′ to f′ from the storage unit 110(such as the storage unit of the smartphone or the cloud storage unit)and makes the two-dimensional positioning points a to f in thetwo-dimensional image 300 correspond to the three-dimensionalpositioning points a′ to f′, respectively, to generate thethree-dimensional model 400.

The user selects any two three-dimensional positioning points (thestarting point and the destination) on the three-dimensional model 400via the input module 160 of the smart device. The data is calculatedbased on the starting point and the destination by the measurementmodule 170 of the smart device. The data is displayed by the outputmodule 180. Thus, the user can know the data about the specific portionof the face (or body) via the measurement device 100.

In an embodiment, when the user uses the application for the measurementmethod to buy a product (such as a glass), the data about the specificportion (such as the inner corner of the eye) of the target object (suchas the face) is measured by establishing the three-dimensional model.After the user obtains the data, the data is provided to a merchantserver. In an embodiment, the data is automatically uploaded to themerchant server via the application. Thus, the merchant can select theproduct in a proper size for the buyer to achieve customization.

In sum, the measurement device and the processor configured to executethe measurement method are provided. The two-dimensional positioningpoints correspond to the three-dimensional positioning points togenerate the three-dimensional model. The data for the specific portionof the target object between any two three-dimensional positioningpoints is measured according to the three-dimensional model. Compared toa two-dimensional measurement, the data for the specific portion of thehuman body that is measured by measuring the data based on the twothree-dimensional positioning points is more precise. The measurementdevice can be applied in online purchases. The data is transmitted tothe merchant. Therefore, the buyer does not need to be at a shop sceneand the merchant provides the customized product for the buyer accordingto the data for the specific portion of the human body.

Although the disclosure has been disclosed with reference to certainembodiments thereof, the disclosure is not for limiting the scope.Persons having ordinary skill in the art may make various modificationsand changes without departing from the scope of the disclosure.Therefore, the scope of the appended claims should not be limited to thedescription of the embodiments described above.

What is claimed is:
 1. A measurement device, adapted to cooperate with athree-dimensional image, the three-dimensional image includes aplurality of three-dimensional positioning points, the measurementdevice comprising: a first camera unit for providing a two-dimensionalimage; an analysis module for analyzing the two-dimensional image todefine a plurality of two-dimensional positioning points in thetwo-dimensional image; a matching module for making the two-dimensionalpositioning points correspond to the three-dimensional positioningpoints, respectively, to generate a three-dimensional model; an inputmodule for receiving a starting point and a destination in thetwo-dimensional image; a measurement module for obtaining first positioninformation and second position information that correspond to thestarting point and the destination, respectively, from thethree-dimensional positioning points, according to the three-dimensionalmodel, and calculating data based on the first position information andthe second position information; and an output module for outputting thedata.
 2. The measurement device according to claim 1, wherein theanalysis module obtains a plurality of human face features from a facefeature database, and compares the human face features with thetwo-dimensional image to obtain a plurality of two-dimensional plottedcoordinates for the two-dimensional positioning points in thetwo-dimensional image.
 3. The measurement device according to claim 1,wherein the measurement device further includes: a storage unit; and asecond camera unit for providing the three-dimensional image and storingthe three-dimensional image into the storage unit.
 4. The measurementdevice according to claim 1, wherein the matching module is configuredto rotate, translate the two-dimensional image, or adjust the size ofthe two-dimensional image, to make the two-dimensional positioningpoints correspond to the three-dimensional positioning points,respectively.
 5. The measurement device according to claim 4, whereinthe matching module is further configured to make the two-dimensionalpositioning points correspond to the three-dimensional positioningpoints, respectively, according to a calibration parameter of a camera.6. A processor configured to execute a measurement method, adapted tocooperate with a three-dimensional image, the three-dimensional imageincludes a plurality of three-dimensional positioning points, theprocessor executes the steps as follows: controlling a camera unit tocapture a target object to obtain a two-dimensional image via a firstcamera unit; analyzing the two-dimensional image to define a pluralityof two-dimensional positioning points in the two-dimensional image;making the two-dimensional positioning points correspond to a pluralityof three-dimensional positioning points, respectively, to generate athree-dimensional model; receiving a starting point and a destination inthe two-dimensional image; obtaining a first position information and asecond position information that correspond to the starting point andthe destination, respectively, from the three-dimensional positioningpoints according to the three-dimensional model and calculating databased on the first position information and the second positioninformation; and outputting the data.
 7. The processor configured toexecute the measurement method according to claim 6, wherein theprocessor executes the step as follows: obtaining a plurality of humanface features from a face feature database, and comparing the human facefeatures with the two-dimensional image to obtain a plurality oftwo-dimensional plotted coordinates for the two-dimensional positioningpoints in the two-dimensional image.
 8. The processor configured toexecute the measurement method according to claim 6, wherein theprocessor is further configured to execute the step as follows:controlling the second camera unit to provide the three-dimensionalimage.
 9. The processor configured to execute the measurement methodaccording to claim 6, wherein the processor is further configured toexecute the step as follows: rotating, translating the two-dimensionalimage, or adjusting the size of the two-dimensional image to make thetwo-dimensional positioning points correspond to the three-dimensionalpositioning points, respectively.
 10. The processor configured toexecute the measurement method according to claim 9, wherein theprocessor executes the step as follows: making the two-dimensionalpositioning points correspond to the three-dimensional positioningpoints, respectively, according to a calibration parameter of a camera.